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Data validation

Data management11/27/2025Basic Level

Data validation is the process of ensuring that data entered or processed in a system is accurate, consistent, and adheres to predefined rules and formats.

What is Data validation? (Definition)

Data validation involves implementing checks and rules to verify the quality and integrity of data. This process happens at various stages, such as data entry, import, or before data is published to external channels. Validation rules can include data type checks (e.g., ensuring a price is a number), format checks (e.g., a SKU follows a specific pattern), range checks (e.g., a product weight falls within acceptable limits), and consistency checks (e.g., a product description is present for all required languages). The goal is to prevent incorrect, incomplete, or inappropriate data from entering or propagating through a system.

Why Data validation is Important for E-commerce

In e-commerce, poor data quality directly impacts customer experience, operational efficiency, and sales. Incorrect product specifications can lead to wrong purchases, high return rates, and negative reviews. Without robust data validation, a PIM system can become a repository of unreliable information, undermining its purpose as a single source of truth. Implementing validation rules ensures that all product data, from descriptions to technical specifications and pricing, meets the required standards before it reaches the customer, thus building trust and reducing costly errors.

Examples of Data validation

  • 1A rule ensuring all product prices are positive numbers and in the correct currency format.
  • 2Validating that every product image URL actually points to an existing digital asset.
  • 3Requiring a 'brand' attribute to be selected from a predefined list of approved brands.
  • 4Checking if a product's 'availability_date' is not in the past for new product launches.
  • 5Enforcing that a product's SKU is unique across the entire product catalog.

How WISEPIM Helps

  • Configurable Validation Rules: WISEPIM allows users to define custom validation rules for any product attribute, ensuring data adheres to specific business requirements.
  • Real-time Error Identification: Identify data quality issues at the point of entry or import, preventing incorrect data from propagating through the system.
  • Automated Compliance Checks: Ensure product data meets compliance standards (e.g., industry regulations, channel-specific requirements) through automated validation.
  • Improved User Productivity: Guide users with clear validation messages, reducing the time spent correcting errors and improving data entry efficiency.

Common Mistakes with Data validation

  • Not defining clear validation rules upfront, leading to inconsistent data quality expectations.
  • Validating data only at the point of entry and neglecting subsequent validation throughout the data lifecycle, allowing errors to creep in during updates or integrations.
  • Over-validating data, creating excessive friction for users and slowing down data input processes without significant quality benefits.
  • Under-validating critical data fields, which permits inaccurate or incomplete information to propagate to external channels, impacting customer experience and sales.
  • Ignoring user feedback on validation errors, failing to refine rules or improve data entry interfaces based on common user struggles.

Tips for Data validation

  • Establish clear data governance policies and standards before implementing validation rules to ensure alignment across the organization.
  • Implement validation at multiple points in the data lifecycle: at data entry, during import processes, and critically, before data is published to any external channel.
  • Prioritize validation for critical data fields (e.g., price, SKU, product identifiers) that directly impact sales, logistics, or customer experience, focusing resources where they matter most.
  • Provide clear, actionable error messages to users, explaining precisely what went wrong and how to correct it, rather than generic error codes.
  • Regularly review and update validation rules as business needs evolve, new data requirements emerge, or common errors are identified.

Trends Surrounding Data validation

  • AI-powered validation: Leveraging AI and machine learning to automatically detect anomalies, suggest validation rules, and predict potential data errors based on historical patterns.
  • Automated data cleansing and enrichment: Integration of validation with automated processes that not only flag errors but also suggest or apply corrections and enrich missing data.
  • Real-time and continuous validation: Shifting from periodic or batch validation to immediate, continuous checks at every point of data interaction, ensuring data quality from creation to publication.
  • Headless commerce implications: Increased need for robust API-driven validation to ensure consistent data quality across diverse frontends and channels in a headless architecture.
  • Sustainability data validation: Development of specific validation rules and frameworks for product sustainability attributes (e.g., certifications, material origins, carbon footprint data) to meet evolving regulatory and consumer demands.

Tools for Data validation

  • WISEPIM: A PIM system that offers extensive data validation capabilities, allowing businesses to define custom rules to ensure product data quality and consistency before omnichannel distribution.
  • Akeneo PIM: Provides a robust framework for defining and enforcing product data validation rules, supporting data enrichment and quality management workflows.
  • Salsify: A Product Experience Management (PXM) platform with built-in data validation features to ensure product content accuracy and completeness across various sales channels.
  • Talend: A data integration and data quality tool that includes powerful capabilities for data profiling, cleansing, and validation across diverse data sources.
  • Magento / Shopify: E-commerce platforms that offer basic product data validation out-of-the-box, often extended by third-party plugins for more sophisticated validation logic.

Related Terms

product-data-qualitydata-governancedata-enrichmentworkflow-managementproduct-attributeproduct-information-management-pim

Also Known As

Data integrity checksData quality controlInput validation